[This post is based on Feruza Bakhtiyorova‘s Bachelor AI thesis]

Oral history interviews are rich sources of personal experiences, but they are often difficult to search. A single interview segment may refer to specific wartime locations, events, organizations, or people, while also touching on broader themes such as persecution, migration, or survival. Making these interviews accessible therefore requires high-quality metadata and meaningful concept annotations.

In her Bachelor’s thesis, Feruza Bakhtiyorova investigated how knowledge graphs can help improve the automatic annotation of oral history interviews from the WO2Net project. The existing workflow uses large language models (LLMs) to link interview segments to concepts from the WO2 Thesaurus, a controlled vocabulary of World War II-related concepts. Earlier work had shown that these links were often too broad or failed to capture important specific concepts such as camps, events, or organizations.

To address this problem, the thesis explored a knowledge graph retrieval-augmented generation (KG-RAG) approach. Instead of asking the language model to choose only from existing candidate concepts, the system first retrieves additional concepts from the WO2 Thesaurus and then asks the model to select the concepts best supported by the interview segment.

The study compared three approaches: baseline prompting, refined prompting, and KG-RAG prompting. Five real oral history segments were evaluated by domain experts, who judged both the correctness and specificity of the assigned concepts.

The results showed a nuanced picture. KG-RAG did not outperform the other methods in overall correctness, but it consistently produced more specific concepts. In several cases it successfully recovered important missing concepts such as Birkenau, Reichenbach, Kristallnacht, and Bergen-Belsen, which were unavailable to the prompting-only approaches. At the same time, retrieving more candidate concepts also increased the risk of introducing less relevant suggestions.

The thesis demonstrates that knowledge graphs can complement large language models by providing richer domain knowledge. While retrieval alone does not guarantee more accurate annotations, it can help generate more precise and historically meaningful concept links, improving access to large oral history collections and supporting future research into cultural heritage archives.

The project’s code can be found at https://github.com/ferufera/wo2-segmentation-optimization

Feruza Bakhtiyorova. Improving Concept Specificity in LLM-Based Concept Linking for Oral History Segments Using the WO2 Thesaurus. Bachelor’s Thesis, Vrije Universiteit Amsterdam, 2026.